where \(\alpha = \widehat{y}^\top y /\Vert y\Vert ^2\)  makes the loss invariant to the scale of the estimated source \(\widehat{y}\)  and the target signal \(y\) . We train all models using the Adam optimizer [1]} with a batch size of \(B=2\)  and an initial learning rate of \(10^{-3}\)  which is divided by 2 every 6 epochs.
